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1,782 results for “algorithms”
Generalized Convulsion: Case Definition Pictorial Algorithm
<p> </p> <p>This post contains the Pictorial Algorithm for Generalized Convulsion. They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
Acute Encephalitis: Case Definition Pictorial Algorithm
<p>This post contains the Pictorial Algorithm for Acute Encephalitis. They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
Anaphylaxis: Case Definition Pictorial Algorithm
<p>This post contains the Pictorial Algorithm for Anaphylaxis. They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
Acute Myelitis: Case Definition Pictorial Algorithm
<p>This post contains the Pictorial Algorithm for Acute Myelitis. They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
Aseptic Meningitis: Case Definition Pictorial Algorithm
<p>This post contains the Pictorial Algorithm for Aseptic Meningitis. They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
Acute Disseminated Encephalomyelitis (ADEM): Case Definition Pictorial Algorithm
<p>This post contains the Pictorial Algorithm for Acute Disseminated Encephalomyelitis (ADEM). They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
Thrombocytopenia: Case Definition Pictorial Algorithm
<p>This post contains the Pictorial Algorithm for Thrombocytopenia. They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
Dataset: A phase field model combined with genetic algorithm for polycrystalline hafnium zirconium oxide ferroelectrics
<p>The folder includes generated data MATLAB scripts to read/plot the polarization-electric field (PE) hysteresis curves. The dataset contains phase field generated polycrystalline grain structure, simulated domain structures during polarization reversal, and symmetric PE curves (measured and simulated).</p> <p><strong>Polycrystalline grain structures</strong>: The output files are in the *.txt format, readable by MTEX to generate orientation maps.<br> Column(1) Column(2) Column(3) Column(4) Column(5)<br> X Y φ(rad) θ(rad) 𝜓(rad)<br> ... ... ... ... ...<br> ... ... ... ... ...<br> ... ... ... ... ...</p> <p><strong>Domain structures</strong>: The output files are in the *.csv format, which can be visualized by programs like ParaView.<br> Column(1) Column(2) Column(3) Column(4)<br> X Y Z P<br> ... ... ... ...<br> ... ... ... ...<br> ... ... ... ...</p> <p><br> <strong>PE curves</strong>: The output files are in the *.txt format, readable by MATLAB.<br> Column(1) Column(2)<br> E(MV/cm) P(μC/cm²)<br> ... ...<br> ... ...<br> ... ...</p> <p><strong>List of datasets:-</strong><br> Fig. 1: pecurves/calib_func1.txt (Calibrated p(e)), pecurves/measpe_hf50.txt (Measured PE curve), pecurves/simpe_calib.txt (Simulated PE curve).<br> Fig. 2(a): polcr_struc/xy_col.txt (XY top view), polcr_struc/yz_col.txt (YZ side view), polcr_struc/xz_col.txt (ZX side view)<br> Fig. 2(b): pecurves/simpe_gaopt.txt (Simulated PE curve), pecurves/measpe_hf50.txt (Measured PE curve).<br> Fig. 3: pecurves/calib_func.txt (Calibrated p(e)), pecurves/gaopt_func.txt (GA optimized p(e)).<br> Fig. 5: pecurves/simpe_gaopt.txt (Case 1), pecurves/simpe_elast.txt (Case 2).<br> Fig. 4(e): dom_struc/dom_profile1.csv, (f) dom_struc/dom_profile2.csv, (g) dom_struc/dom_profile3.csv, (h) dom_struc/dom_profile4.csv, (m) dom_struc/dom_profile5.csv, (n) dom_struc/dom_profile6.csv, (o) dom_struc/dom_profile7.csv, (p) dom_struc/dom_profile8.csv<br> Fig. 6: pecurves/simpe_gaopt.txt (GA fit coefficients), pecurves/simpe_ldc1.txt (Set 1), pecurves/simpe_ldc2.txt (Set 2).<br> Fig. 7(a): pecurves/simpe_gaopt.txt (𝝂₀ = 1.0), pecurves/simpe_fr80.txt (𝝂₀ = 0.8), pecurves/simpe_fr50.txt (𝝂₀ = 0.5).<br> Fig. 7(b): pecurves/measpe_hf50.txt (Hf₀.₅Zr₀.₅O₂), pecurves/measpe_hf75.txt (Hf₀.₇₅Zr₀.₂₅O₂).<br> Fig. 8: pecurves/simpe_fr38.txt (Simulated PE curve), pecurves/measpe_hf75.txt (Measured PE curve).<br> Fig. 9: pecurves/simpe_gaopt.txt (Random non-textured), pecurves/simpe_tex001.txt ([001] fiber textured), pecurves/simpe_tex111.txt ([111] fiber textured).<br> Fig. 10(a): polcr_struc/xy_equ.txt (XY top view), polcr_struc/yz_equ.txt (YZ side view), polcr_struc/xz_equ.txt (ZX side view)<br> Fig. 10(b): pecurves/simpe_colmor.txt (Columnar grain microstructure), pecurves/simpe_equmor.txt (Equiaxed grain microstructure).</p> <p><strong>List of MATLAB scripts:</strong><br> Fig 1: matlab_scripts/fig1.m<br> Fig 2(b): matlab_scripts/fig2b.m<br> Fig 3: matlab_scripts/fig3.m<br> Fig 5: matlab_scripts/fig5.m<br> Fig 6: matlab_scripts/fig6.m<br> Fig 7(a): matlab_scripts/fig7a.m<br> Fig 7(b): matlab_scripts/fig7b.m<br> Fig 8: matlab_scripts/fig8.m<br> Fig 9: matlab_scripts/fig9.m<br> Fig 10(b): matlab_scripts/fig10b.m<br> </p>
Figure 6 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 6. Motion of colonies toward their relevant imperialist (AtashpazGargari 2009).
Figure 4 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 4. Flowchart of Imperialist Competitive Algorithm (AtashpazGargari 2009).
Figure 7 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 7. Distribution of T. urticae in different stages of sampling.
Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"
<p>Visualize the data in this dataset: <a href="https://www.c6h6.org/zenodo/record/?id=7186602">open entry</a>.</p>
Eurasian lynx GLCs' characteristics for classification with random forest algorithm
<p><span>Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill site identification, such as failing to detect GLCs that are kill sites and misclassifying the generated GLCs (e.g. kill for non-kill) that were not field-checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx, an apex predator of conservation concern in Europe, in three multi-prey systems, with different combinations of wild, semi-domestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affect the detection of GLCs indicating kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as non-kills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced to from 7 to 3 fixes/night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24-h decreased the probability of detecting GLCs connected with kill sites, particularly those of semi-domestic or domestic prey, and small prey. Random forest successfully predicted between 73%-90% of ungulate kills but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm's overall accuracy. We provide a set of recommendations for studies focusing on kill site detection, which can be considered for other large carnivore species besides the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.</span></p>
To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features - Dataset
<p>This repository contains the reproduction steps and intermediate artifacts corresponding to the paper 'To Switch or not to Switch:<br> Predicting the Benefit of Switching between Algorithms based on Trajectory Features'. During the submission process, this repository is anonymized to our best ability. </p> <p>The file 'README' contains the description of which file contains what data, and how they correspond to different parts of the paper.</p>
Interplay between Genetics, Epigenetics & Exposures- Proposed Algorithm for Pediatric Cancer Development
Interplay between Genetics, Epigenetics & Exposures- Proposed Algorithm for Pediatric Cancer Development
Additional datasets and code accompanying the article "Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines"
<p> </p> <p> </p> <p>Datasets and code accompanying the manuscript titled “Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines” by Rajarshi Ghosh, Ninak Oak and Sharon E. Plon.</p>
DIN: A Decentralized Inexact Newton Algorithm for Consensus Optimization: SW and Data
<p>This upload contains the main simulation code and related datasets used in the conference paper entitled <a href="https://ieeexplore.ieee.org/document/10278721" target="_blank" rel="nofollow noreferrer noopener">DIN: A Decentralized Inexact Newton Algorithm for Consensus Optimization</a>, which was presented at <a href="https://icc2023.ieee-icc.org/" target="_blank" rel="nofollow noreferrer noopener">IEEE ICC 2023</a>.</p>
Figure 1 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 1. Examples of images collected for soybean in the VE-VC (A) and R2 (B) growth stages.
ICESat-2 Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: Coastal Alaska
<p>This dataset is derived from the ICESat-2 Global Geolocated Photon Height Product (ATL03, <a href="https://nsidc.org/data/atl03/versions/6" target="_blank" rel="noopener">Neumann et al., 2023</a>), release 006, using the University of Maryland-Ridge Detection Algorithm (UMD-RDA, <a href="https://doi.org/10.1029/2022GL100272" target="_blank" rel="noopener">Duncan & Farrell, 2022</a>). The UMD-RDA is applied to ATL03 granules on a per-shot basis, nominally resulting in elevation measurements at ICESat-2's along-track sampling of ~0.7 m.</p> <p><strong>Data Bounds (lon,lat): </strong></p> <p><em>LL corner: (-169.71, 68.32), UR corner: (-137.1, 72.18)</em></p> <p> </p> <p><strong>File Format</strong></p> <p>Coastal Alaska data are provided in .xz compressed format and decompress to ascii text. The columns are as follows:</p> <ol> <li>Longitude (degrees)</li> <li>Latitude (degrees)</li> <li>Time (seconds since 2018-01-01)</li> <li>Elevation (meters, relative to DTU18 Mean Sea Surface)</li> </ol> <p>Data are provided monthly for the period December 2021 (202112) to May 2022 (202205).</p> <p> </p> <p><strong>Users of this dataset are asked to cite the following publication:</strong></p> <p><em>Duncan, K. and Farrell, S. L. (2022). Determining Variability in Arctic Sea Ice Pressure Ridge Topography with ICESat-2. Geophys. Res. Lett., 49, e2022GL100272. https://doi.org/10.1029/2022GL100272</em></p> <p> </p> <p><em>This dataset is supported by NASA Cryosphere Grant 80NSSC20K0966</em></p> <p> </p> <p>For any questions regarding this dataset you can contact Kyle Duncan by email: kduncan at umd . edu</p>
Simulation results for "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes"
<p>This dataset contains simulations results presented in the paper "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes".</p> <p>The files are in `csv` file format, with data easily processable using the python library `sinter`.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.